The present invention relates in general to a method and system for comparing and maximizing the appropriateness of a group of entities having capabilities for fulfilling a mission having requirements, where the entities and mission are represented by unstructured data, structured data, and constraints.
Latent Semantic Analysis
Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by mathematical computations (namely singular value decomposition) applied to a large corpus of text. The underlying concept is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other.
Probabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis (PLSA) is one of many unsupervised statistical machine-learning techniques that posit latent “topics,” a mixture of which is modeled as generating the words in a document. Additionally, the topics themselves are easily interpretable and thus become an explanatory component for visualization, analysis, and allow for spot inspection of performance.
The PLSA model has been shown to be more accurate than LSA for information retrieval (Hofmann, 1999), and has been deployed in legal, medical and publishing domains with large knowledge repositories.
Matching Resumes and Job Descriptions
Both LSA (Laham, et al. 2000) and PLSA (U.S. Pat. No. 6,728,695, Pathria et al., 2004) have been used to match a single entity represented by unstructured and structured data to a mission represented by unstructured and structured data. The entities in these cases were individuals represented by resumes, and the missions were occupations represented by job postings.
However, methods have not been disclosed that utilize LSA or PLSA to determine the appropriateness of a group of entities for accomplishing the goals of a single mission or a multitude of missions.